Vision and Foraging in Cormorants: More like Herons than Hawks?

Background Great cormorants (Phalacrocorax carbo L.) show the highest known foraging yield for a marine predator and they are often perceived to be in conflict with human economic interests. They are generally regarded as visually-guided, pursuit-dive foragers, so it would be expected that cormorants have excellent vision much like aerial predators, such as hawks which detect and pursue prey from a distance. Indeed cormorant eyes appear to show some specific adaptations to the amphibious life style. They are reported to have a highly pliable lens and powerful intraocular muscles which are thought to accommodate for the loss of corneal refractive power that accompanies immersion and ensures a well focussed image on the retina. However, nothing is known of the visual performance of these birds and how this might influence their prey capture technique. Methodology/Principal Findings We measured the aquatic visual acuity of great cormorants under a range of viewing conditions (illuminance, target contrast, viewing distance) and found it to be unexpectedly poor. Cormorant visual acuity under a range of viewing conditions is in fact comparable to unaided humans under water, and very inferior to that of aerial predators. We present a prey detectability model based upon the known acuity of cormorants at different illuminances, target contrasts and viewing distances. This shows that cormorants are able to detect individual prey only at close range (less than 1 m). Conclusions/Significance We conclude that cormorants are not the aquatic equivalent of hawks. Their efficient hunting involves the use of specialised foraging techniques which employ brief short-distance pursuit and/or rapid neck extension to capture prey that is visually detected or flushed only at short range. This technique appears to be driven proximately by the cormorant's limited visual capacities, and is analogous to the foraging techniques employed by herons

Voxel-wise comparisons of cellular microstructure and diffusion-MRI in mouse hippocampus using 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND)

A key challenge in medical imaging is determining a precise correspondence between image properties and tissue microstructure. This comparison is hindered by disparate scales and resolutions between medical imaging and histology. We present a new technique, 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND), for registering medical images with 3D histology to overcome these limitations. Ex vivo 120 × 120 × 200 μm resolution diffusion-MRI (dMRI) data was acquired at 7 T from adult C57Bl/6 mouse hippocampus. Tissue was then optically cleared using CLARITY and stained with cellular markers and confocal microscopy used to produce high-resolution images of the 3D-tissue microstructure. For each sample, a dense array of hippocampal landmarks was used to drive registration between upsampled dMRI data and the corresponding confocal images. The cell population in each MRI voxel was determined within hippocampal subregions and compared to MRI-derived metrics. 3D-BOND provided robust voxel-wise, cellular correlates of dMRI data. CA1 pyramidal and dentate gyrus granular layers had significantly different mean diffusivity (p > 0.001), which was related to microstructural features. Overall, mean and radial diffusivity correlated with cell and axon density and fractional anisotropy with astrocyte density, while apparent fibre density correlated negatively with axon density. Astrocytes, axons and blood vessels correlated to tensor orientation

A de novo designed protein-protein interface

As an approach to both explore the physical/chemical parameters that drive molecular self-assembly and to generate novel protein oligomers, we have developed a procedure to generate protein dimers from monomeric proteins using computational protein docking and amino acid sequence design. A fast Fourier transform-based docking algorithm was used to generate a model for a dimeric version of the 56-amino-acid β1 domain of streptococcal protein G. Computational amino acid sequence design of 24 residues at the dimer interface resulted in a heterodimer comprised of 12-fold and eightfold variants of the wild-type protein. The designed proteins were expressed, purified, and characterized using analytical ultracentrifugation and heteronuclear NMR techniques. Although the measured dissociation constant was modest (~300 µM), 2D-[^1H,^(15)N]-HSQC NMR spectra of one of the designed proteins in the absence and presence of its binding partner showed clear evidence of specific dimer formation

THUMP from archaeal tRNA:m(2)(2)G10 methyltransferase, a genuine autonomously folding domain

The tRNA:m(2)(2)G10 methyltransferase of Pyrococus abyssi (PAB1283, a member of COG1041) catalyzes the N(2),N(2)-dimethylation of guanosine at position 10 in tRNA. Boundaries of its THUMP (THioUridine synthases, RNA Methyltransferases and Pseudo-uridine synthases)—containing N-terminal domain [1–152] and C-terminal catalytic domain [157–329] were assessed by trypsin limited proteolysis. An inter-domain flexible region of at least six residues was revealed. The N-terminal domain was then produced as a standalone protein (THUMPα) and further characterized. This autonomously folded unit exhibits very low affinity for tRNA. Using protein fold-recognition (FR) methods, we identified the similarity between THUMPα and a putative RNA-recognition module observed in the crystal structure of another THUMP-containing protein (ThiI thiolase of Bacillus anthracis). A comparative model of THUMPα structure was generated, which fulfills experimentally defined restraints, i.e. chemical modification of surface exposed residues assessed by mass spectrometry, and identification of an intramolecular disulfide bridge. A model of the whole PAB1283 enzyme docked onto its tRNA(Asp) substrate suggests that the THUMP module specifically takes support on the co-axially stacked helices of T-arm and acceptor stem of tRNA and, together with the catalytic domain, screw-clamp structured tRNA. We propose that this mode of interactions may be common to other THUMP-containing enzymes that specifically modify nucleotides in the 3D-core of tRNA

Protein docking by Rotation-Based Uniform Sampling (RotBUS) with fast computing of intermolecular contact distance and residue desolvation

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions are fundamental for the majority of cellular processes and their study is of enormous biotechnological and therapeutic interest. In recent years, a variety of computational approaches to the protein-protein docking problem have been reported, with encouraging results. Most of the currently available protein-protein docking algorithms are composed of two clearly defined parts: the sampling of the rotational and translational space of the interacting molecules, and the scoring and clustering of the resulting orientations. Although this kind of strategy has shown some of the most successful results in the CAPRI blind test <url>http://www.ebi.ac.uk/msd-srv/capri</url>, more efforts need to be applied. Thus, the sampling protocol should generate a pool of conformations that include a sufficient number of near-native ones, while the scoring function should discriminate between near-native and non-near-native proposed conformations. On the other hand, protocols to efficiently include full flexibility on the protein structures are increasingly needed.</p> <p>Results</p> <p>In these work we present new computational tools for protein-protein docking. We describe here the RotBUS (Rotation-Based Uniform Sampling) method to generate uniformly distributed sets of rigid-body docking poses, with a new fast calculation of the optimal contacting distance between molecules. We have tested the method on a standard benchmark of unbound structures and we can find near-native solutions in 100% of the cases. After applying a new fast filtering scheme based on residue-based desolvation, in combination with FTDock plus pyDock scoring, near-native solutions are found with rank ≤ 50 in 39% of the cases. Knowledge-based experimental restraints can be easily included to reduce computational times during sampling and improve success rates, and the method can be extended in the future to include flexibility of the side-chains.</p> <p>Conclusions</p> <p>This new sampling algorithm has the advantage of its high speed achieved by fast computing of the intermolecular distance based on a coarse representation of the interacting surfaces. In addition, a fast desolvation scoring permits the screening of millions of conformations at low computational cost, without compromising accuracy. The protocol presented here can be used as a framework to include restraints, flexibility and ensemble docking approaches.</p

A Comprehensive Profile of Decoding and Comprehension in Autism Spectrum Disorders

The present study examined intake data from 384 participants with autism spectrum disorders (ASD) and a comparison group of 100 participants with dyslexia on nine standardized measures of decoding and comprehension. Although diagnostic groups were based on parental reports and could not be verified independently, we were able to observe significant distinctions between subject groups. Overall findings confirm previous results of a disassociation between decoding and comprehension in ASD. Using a larger sample than previous studies and a greater variety of measures, a pattern of relatively intact decoding skills paired with low comprehension was found in autism, PDD-NOS, and Asperger’s. In contrast, the dyslexic group showed the opposite pattern of stronger comprehension and weaker decoding